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To cope with uncertainties concerning the estimation of the model parameters (e.g. transfer and partitioning coefficients, emission factors) as well as uncertainties about the exposure causal mechanisms (e.g. level of compound production and application) themselves, we utilized and combined sensitivity and uncertainty analysis, Monte Carlo simulation and Markov Chain Monte Carlo modeling.
Monte Carlo Modeling of Light Transport (MCMLT) has been run for the head model and photon fluencies have been generated.
When the results of the Monte Carlo modeling are evaluated and integrated, practical constraints are part of defining the potential context in which a given technology might be applied.
NRMC is a parallel code for performing N-Reverse Monte Carlo modeling of fluids in confined media [V. Sánchez-Gil, E.G. Noya, E. Lomba, J. Chem. Phys. 140 (2014) 024504].
Most of the computational time in N-Reverse Monte Carlo modeling is spent in the evaluation of the structure factor for each trial configuration, a calculation that can be easily parallelized.
In this contribution, using Monte Carlo modeling, we show how simple molecular building blocks with cruciform shape can be used to create 2D porous matrices with tunable adsorptive properties.
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Chapter 5 will introduce the use of Monte-Carlo modeling for scaling the collected data to adjust the detected coincidence counts for losses due to positron escape from plant tissue.
Periodic Monte Carlo models with 125 identical inclusions are studied.
The simulator is a probabilistic Monte Carlo model that uses statistics and results from the regular season.
The Monte Carlo model was more accurate than the six-flux model.
We have also developed a football simulator based on a probabilistic Monte Carlo model to use in conjunction with our championship factors.
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